An individual assignment makes up your final grade for the course. The assignment consists of a research report which addresses the influence of personality on online social behavior. The assignment will be marked with a 0.1 and counted as an attempt, with no further feedback given, if any plagiarism is found. This term, describe and analyze how personality affects tourists’ online social behavior from a quantitative perspective. In your literature review you should adopt a funnel approach. In other words, you start out broad by describing what academics consider personality, provide descriptions etc. Next, narrow your discussion and explain to the reader how personality is related to behavior, specifically tourist behavior. Possibly make inferences on how personality could be associated with tourists' online social behavior. Finally, focus on a specific aspect of personality (e.g., sensation seeking) or a wider range of personality traits (e.g., the Big 5) and convince the reader that this aspect is the most interesting predictor for a certain online behavior. It will include: · Review academic literature; · Construct relevant research questions; · To set-up a proper questionnaire; · To analyze the quantitative data; · Compare the analysis of the primary data to the literature review; · Make recommendations based on quantitative research findings; · Write a professional report according to APA guidelines. Please do not include all the individually filled out questionnaires in your report. One (not filled out) copy in the appendix will do. The actual survey may be in any language, granted you put an English version in an appendix. You will collect (at least) 40 responses to your questionnaire. We recommend using one of the following references for the measurement of personality in your survey : Barnett, L. A. (2007). The Nature of Playfulness in Young Adults. Personality and Individual Differences, 43(4), 949–958. Gosling, S. D., Rentfrow, P. J., & Jr., W. B. S. (2003). A Very Brief Measure of the Big-Five Personality Domains. Journal of Research in Personality, 37(6), 504–528. Hoylea, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P., & Donohew, R. L. (2002). Reliability and Validity of a Brief Measure of Sensation Seeking. Personality and Individual Differences, 32(3), 401–414. Rammstedt, B., & John, O. P. (2007). Measuring Personality in One Minute or Less: A 10Item Short Version of the Big Five Inventory in English and German. Journal of Research in Personality, 41(1), 203–212. The assignment will be marked with a 0.1 and counted as an attempt, with no further feedback given, if any plagiarism is found. The first page of the report must include the sentence, “I, (your name), have read the plagiarism appendix to the course outline, and I am absolutely certain that my report contains no plagiarism.” Otherwise we will not begin to mark it or give feedback. The work includes: Literature review, Questionnaire preparati.

1. An individual assignment makes up your final grade for the
course. The assignment consists of a research report which
addresses the influence of personality on online social behavior.
The assignment will be marked with a 0.1 and counted as an
attempt, with no further feedback given, if any plagiarism is
found.
This term, describe and analyze how personality affects
tourists’ online social behavior from a quantitative perspective.
In your literature review you should adopt a funnel approach. In
other words, you start out broad by describing what academics
consider personality, provide descriptions etc. Next, narrow
your discussion and explain to the reader how personality is
related to behavior, specifically tourist behavior. Possibly make
inferences on how personality could be associated with tourists'
online social behavior. Finally, focus on a specific aspect of
personality (e.g., sensation seeking) or a wider range of
personality traits (e.g., the Big 5) and convince the reader that
this aspect is the most interesting predictor for a certain online
behavior. It will include:
· Review academic literature;
· Construct relevant research questions;
· To set-up a proper questionnaire;
· To analyze the quantitative data;
· Compare the analysis of the primary data to the literature
review;
· Make recommendations based on quantitative research
findings;
· Write a professional report according to APA guidelines.
Please do not include all the individually filled out
questionnaires in your report. One (not filled out) copy in the
appendix will do. The actual survey may be in any language,
granted you put an English version in an appendix. You will
collect (at least) 40 responses to your questionnaire.

2. We recommend using one of the following references for the
measurement of personality in your survey :
Barnett, L. A. (2007). The Nature of Playfulness in Young
Adults. Personality and Individual Differences, 43(4), 949–958.
Gosling, S. D., Rentfrow, P. J., & Jr., W. B. S. (2003). A Very
Brief Measure of the Big-Five Personality Domains. Journal of
Research in Personality, 37(6), 504–528.
Hoylea, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P.,
& Donohew, R. L. (2002). Reliability and Validity of a Brief
Measure of Sensation Seeking. Personality and Individual
Differences, 32(3), 401–414.
Rammstedt, B., & John, O. P. (2007). Measuring Personality in
One Minute or Less: A 10Item Short Version of the Big Five
Inventory in English and German. Journal of Research in
Personality, 41(1), 203–212.
The assignment will be marked with a 0.1 and counted as an
attempt, with no further feedback given, if any plagiarism is
found. The first page of the report must include the sentence, “I,
(your name), have read the plagiarism appendix to the course
outline, and I am absolutely certain that my report contains no
plagiarism.” Otherwise we will not begin to mark it or give
feedback.
The work includes: Literature review, Questionnaire
preparation, Data collection, Data analysis in SPSS, Report
writing
An individual assignment makes up your final grade for the
course. The assignment consists of a
research report which addresses the
influence of personality on online social behavior
. The

3. assignment will be
marked with a 0.1 and counted as an attempt, with no further
feedback given,
if any plagiarism is found
.
This term,
describe and analyze how personality affects
tourists’
online s
ocial behavior from a
quantitative perspective.
In your literature review you should adopt a funnel approach. In
other
words, you start out broad by describing what academics
consider personality, provide
descriptions etc. Next, narrow your discussion and
explain to the reader how personality is
related to behavior, specifically tourist behavior. Possibly make
inferences on how personality
could be associated with tourists' online social behavior.
Finally, focus on a specific aspect of
personality (e.g., se
nsation seeking) or a wider range of personality traits (e.g., the
Big 5) and
convince the reader that this aspect is the most interesting
predictor for a certain online
behavior.
It will include:
-
Review academic literature;

4. · Construct relevant research questions;
· To set
-
up a proper questionnaire;
· To analyze the quantitative data;
· Compare the analysis of the primary data to the literat
ure review;
· Make recommendations based on quantitative research
findings;
· Write a professional report according to APA guidelines.
Please do not include all the individually filled out
questionnaires in yo
ur report. One (not filled
out) copy in the appendix will do. The actual survey may be in
any language, granted you put an
English version in an appendix. You will collect (at least) 40
responses to your questionnaire.
We recommend using one of the follo
wing references for the measurement of personality in your
survey
:
Barnett, L. A. (2007). The Nature of Playfulness in Young
Adults. Personality and Individua
l
Differences, 43(4), 949
–
958.

5. Gosling, S. D., Rentfrow, P. J., & Jr., W. B. S. (2003). A Very
B
rief Measure of the Big
-
Five
Personality Domains. Journal of Research i
n Personality, 37(6), 504
–
528.
Hoylea, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P.,
& Donohew, R. L. (2002). Reliability
and Validity of a Brief Measure of Sensation Seeking.
Personality and Individua
l Differences,
32(3), 401
–
414.
An individual assignment makes up your final grade for the
course. The assignment consists of a
research report which addresses the influence of personality on
online social behavior. The
assignment will be marked with a 0.1 and counted as an attempt,
with no further feedback given,
if any plagiarism is found.
This term, describe and analyze how personality affects
tourists’ online social behavior from a
quantitative perspective. In your literature review you should
adopt a funnel approach. In other
words, you start out broad by describing what academics
consider personality, provide
descriptions etc. Next, narrow your discussion and explain to
the reader how personality is

6. related to behavior, specifically tourist behavior. Possibly make
inferences on how personality
could be associated with tourists' online social behavior.
Finally, focus on a specific aspect of
personality (e.g., sensation seeking) or a wider range of
personality traits (e.g., the Big 5) and
convince the reader that this aspect is the most interesting
predictor for a certain online
behavior. It will include:
- Review academic literature;
· Construct relevant research questions;
· To set-up a proper questionnaire;
· To analyze the quantitative data;
· Compare the analysis of the primary data to the literature
review;
· Make recommendations based on quantitative research
findings;
· Write a professional report according to APA guidelines.
Please do not include all the individually filled out
questionnaires in your report. One (not filled
out) copy in the appendix will do. The actual survey may be in
any language, granted you put an
English version in an appendix. You will collect (at least) 40
responses to your questionnaire.
We recommend using one of the following references for the
measurement of personality in your
survey :
Barnett, L. A. (2007). The Nature of Playfulness in Young
Adults. Personality and Individual
Differences, 43(4), 949–958.
Gosling, S. D., Rentfrow, P. J., & Jr., W. B. S. (2003). A Very
Brief Measure of the Big-Five
Personality Domains. Journal of Research in Personality, 37(6),
504–528.
Hoylea, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P.,

7. & Donohew, R. L. (2002). Reliability
and Validity of a Brief Measure of Sensation Seeking.
Personality and Individual Differences,
32(3), 401–414.
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QUANT DATA ANALYSIS
QUANT DATA ANALYSIS

137. Measurement
Inference
Description
2
We analyze quant data using statistics
Statistics are not reality, truth, or “proof” of something!
Statistics are MODELS: a representation of reality
A way to learn ROUGHLY how customers’ behavior
looks/feels
is not
is a MODEL of
The data from questionnaires measures responses on
variables, possibly at different levels
Railway services
in NL
Company Speed Speed (km/h)
Arriva Spurt DB Slow 120
Intercity NS Medium 150
IC Direct NS Hispeed Medium 200

138. ICE DB Fast 300
NOMINAL ORDINAL SCALE
This flowchart helps determine level of
measurement
NOMINAL LEVEL
SCALE LEVEL
ORDINAL LEVEL
Are the distances between the
successive values equal?
Yes
No
Is it possible to order the different
values?
Yes
No
Finding level of measurement for each variable is step 0 in
analysis
Analyze variables first one at a time, by describing

139. the response to each
What was the average response on this variable?
How spread out were the responses on this variable?
Days per week checking online news average in this
sample = 3.5
A rough MODEL for
how often your
population checks
online news
Your choice of correct descriptive statistics depends
on level of measurement
Variable is…… Central tendency
(average)
Spread
Nominal Mode Frequency
Ordinal Median Range
Scale Mean Standard deviation*
What was the average response on this variable?
How spread out were the responses on this variable?

140. *Standard deviation represents the differences
between the mean and each response
An average (mean) of 3.5 days per week checking online news
does not
imply that everyone checked 3.5 days per week.
If the responses are distributed normally (“bell curve”), then 2/3
of responses
are between mean +/- std deviation
0 7 (max)
3.5 (average)
Analyze variables first one at a time, by describing
the response to each
What was the average response on this variable?
How spread out were the responses on this variable?
Days per week checking online news average in this
sample = 3.5
std. dev. in this
sample = 0.8
So a larger standard deviation (on the same scale) =
responses were more spread out
Lloret de Mar
Ibiza

141. Descriptive statistics describe the sample and model
the population average and spread
Based on assumptions of a probability sample and
normal-distributed (“bell curve”) population
How about a model of the effect of on variable on
another?
Then we could (tentatively)
answer our research questions
online behavior
pe
rs
on
al
ity
For two scale or ordinal variables, we use models
called correlation
Extraversion

142. +/- correlation indicates the direction of the
relationship
Extraversion
POSITIVE NEGATIVE
Age
The further a correlation is from 0, the stronger the
relationship
Age
0.9 -0.9
0.1 -0.4
-0.1 0
Some rules of thumb for correlation strength
Correlation Positive Negative
Small 0 to 0.3 -0.3 to 0
Medium 0.3 to 0.5 -0.5 to -0.3
Strong 0.5 to 1 -1 to -0.5
When researching people, “strong” correlations are rare

143. For variables with 2 categories like gender (nominal), code
categories as 0 and 1 to use correlation
3 tweets/week
Male=0
Here one expects a positive correlation between gender
tweets/week. As gender increases (1 instead of 0),
tweets/week also increase. Conclusion: women tweet more
Two categories coded as 0 and 1 work like a scale variable
in correlation
Female =1
2 tweets/week
3 tweets/week
7 tweets/week
So, choose model based on level of measurement
Variable 1 Variable 2 Appropriate model
Nominal (2 categories
coded 0 and 1)
Scale! Correlation (Pearson)

144. Ordinal Correlation
(Spearman)
Scale Correlation (Pearson)
Furthermore we need to know the chances that our
model reflects a real effect in the population
Suppose you have a tour operator
with 10 customers, 5 male and 5
female.
To decide on marketing through a
new women-oriented online portal,
you need to know if there is a
difference in satisfaction between
the men & women
Satisfaction from a sample
4
8
4
6
mean=6 mean=5
Does this difference reflect a difference in the population (all
10 customers) or is it a coincidence?

145. Could be a real difference or could be a coincidence
of the sample you took
4
8
4
6
8 6
4 5
6 4
mean=6 mean=5
4
8
4
6
8 6
4 8
6 6
mean=6 mean=6

146. Sig. = % chance that an effect in the sample is
coincidence and not from an effect in the population
4
8
4
6
8 6
4 8
6 6
mean=6 mean=6
4
8
4
6
mean=6 mean=5
Again, sig. = chance that you
found an effect in the sample…
…even though there is NO effect

147. in your population
When interpreting output, first look if sig.< 0.05
95% probability that effect did not happen by chance
FIRST look at the sig: Is there an effect?
ONLY if sig. <0.05 conclude, YES
THEN look at correlation / means: How strong and in
which direction is the effect?
Example correlation (1)
Number of facebook
friends
Extraversion
Pearson correlation
Sig (2-tailed)
N
.619**
.001
763
The significance level is lower than .05 (i.e. .001). This
means we conclude that extraversion is significantly

148. related to the number facebook friends people had.
Example correlation (2)
The significance level is higher than .05 (i.e. .583). This
means we conclude that calories eaten/day is not significantly
related to the number of trips people made in 2015.
# of trips taken in 2015
Calories eaten/day
Pearson correlation
Sig (2-tailed)
N
.003
.583
763
QUANT DATA ANALYSISQUANT DATA ANALYSISWe
analyze quant data using statisticsThe data from questionnaires
measures responses on variables, possibly at different
levelsThis flowchart helps determine level of
measurementAnalyze variables first one at a time, by describing
the response to eachYour choice of correct descriptive statistics
depends on level of measurement *Standard deviation
represents the differences between the mean and each
responseAnalyze variables first one at a time, by describing the
response to eachSo a larger standard deviation (on the same
scale) = responses were more spread outDescriptive statistics
describe the sample and model the population average and
spreadHow about a model of the effect of on variable on
another? For two scale or ordinal variables, we use models

149. called correlation +/- correlation indicates the direction of the
relationshipThe further a correlation is from 0, the stronger the
relationshipSome rules of thumb for correlation strengthFor
variables with 2 categories like gender (nominal), code
categories as 0 and 1 to use correlationSo, choose model based
on level of measurementFurthermore we need to know the
chances that our model reflects a real effect in the
populationSatisfaction from a sampleCould be a real difference
or could be a coincidence of the sample you tookSig. = %
chance that an effect in the sample is coincidence and not from
an effect in the population When interpreting output, first look
if sig.< 0.05
95% probability that effect did not happen by chance Example
correlation (1)Example correlation (2)
©
Quantitative Research for ITTI
Lecture 3
Population and Sampling
©
Sampling is the process of selecting
cases to participate in your study
Compose frame

150. Define population
Solve frame problems
Choose census or
sample Design sampling
procedure
Determine sample size
Validate sample
Choose type of sample
©
First, define the population, the collection of
elements about which inferences are to be made
Elements
could be
institutions,
persons,
groups
Specify
elements,
place, time
©

151. Second, obtain the sampling frame, a list of all
elements in the population
Sampling frame: List of
names and addresses of X’s
Tokyo-bound customers
______________________
_______________________
_______________________
____________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
Population: Tourists visiting Tokyo with Tour
Operator X in May 2013
©
Third, identify and solve any problems with the
frame
Sampling frame: List of
names and addresses of
X’s Tokyo-bound
customers
Alinda Kokkinou
John Smith, Sr.

152. Bob Jones
Jeroen Nawijn
Bob Jones
Stephen Mattes
Jana Machova
John Smith, Jr.
Alien names: not on trip…
Sometimes you have duplication…
Missing: Bob’s brother Frank
©
Fourth, decide if you will use a census or sample
Would you have enough resources (time,
money, people) to study the entire
population?
How big is the population? (up to
1000, a census may be worthwhile)
SAMPLE CENSUS
©
Fifth, choose a probability and a non-probability
sample, depending on need to generalize
Unequal (or unknown) chance of
being selected

153. Everyone has the same (or a
known) probability of being
selected
PROBABILITY NON-PROBABILITY
can can not
©
Sixth, determine the sample size you need
according to your planned analysis……
favorite SM fb tw tu inst snpcht total
business
tourist
total N =
250
Base the plan on your largest possible
cross-table
You want minimum 25
cases per cell
2 rows 5 columns 25 250 x x =
©
……or, according to a formula

154. Precision: 3% (0,03)
Confidence level: 95% (z = 1,96)
Variability: P = 0,5 (q=1-p)
0,03² = 1,96 ² x 0,25n
0,0009 = 0,9604n n = 1067
Depends on heterogeneity of
population…
level of
precision…
level of
confidence…
…not on population size.
www.surveysystem.com/sscalc.htm
(or google ‘sample size calculator’)
©
1
©

155. ©
©
©
©
©
©
©
©
©

156. ©
©
©
©
©
©
Seventh, plan in terms of collecting gross sample
size so your net sample is large enough
Refuses to participate
Add expected non-response to calculated sample size
You messed up their address
Dies during study
© Eight, decide on a specific type of sample, either a

157. probability sample……
Random sample––every
individual has equal chance
of being selected
Systematic
sample––every Nth
individual
1st
7th
3rd
5th
Stratified sample––
random sample within
each subgroup
© ……or a non-probability sample, which is not
generalizable but often easier
Convenience/opportunity
sample Select whoever is
available
Snowball sample Ask
individuals to suggest other
potential participants
Quota sample

158. Sample at least
minimum from each
subgroup
© Once you select your sample, the way you contact
them can greatly impact the response you get
Post?
Email? In-person?
© Finally, validate your sample
=
Does sample distribution on
prominent variables resemble the
population distribution?
[Have to know something about
population]
% % %
Choose a variable that’s known
in the population and check if
your sample is not too different
©

159. Read before workshop:
book 4th ed.: pp. 330-339
Please bring your book to the workshop, a print copy of the
Course Outline and a print copy of your survey.
Read before next lecture:
-350
Slide Number 1Slide Number 2Slide Number 3Slide Number
4Slide Number 5Slide Number 6Slide Number 7Slide Number
8Slide Number 9Slide Number 10Slide Number 11Slide Number
12Slide Number 13Slide Number 14Slide Number 15Slide
Number 16Slide Number 17Slide Number 18Slide Number
19Slide Number 20Slide Number 21Slide Number 22Slide
Number 23Slide Number 24Slide Number 25Slide Number
26Slide Number 27Slide Number 28Slide Number 29Slide
Number 30Slide Number 31
QUESTIONNAIRE DESIGN
QUESTIONNAIRE DESIGN
Defintion
DONT’s

160. Design tips
DO’s
2
Most important principles are……
1. Get a number whenever you can
2. Make it clear from the respondent’s
point of view.
2
A questionnaire is an ordered form designed to
measure variables
Like a ruler for behavior!
2
Questionnaires are based on the concepts from
your lit review and research questions
In quant research, we usually ask “what is effect of X on Y?”
For example, what is effect of extraversion on freq of Twitter
use?

161. Point of a questionnaire is to measure X and Y in each person
X Y
Extraversion Twitter use
A questionnaire gets a number for each variable,
one at a time
extraversion Twitter use
Your framework/theory:
Your questionnaire:
—————
—————
—————
—————
Items measuring extraversion
Items measuring Twitter use
2
When possible, copy your items exactly from
existing research!
Unlike in writing, it’s not plagiarism.
You have to credit your source in your paper (methods section)

162. but not in your questionnaire.
Do (1) get a number whenever you can
X What age are you?
O 16-20 years
O 20-25 years
O 25-30 years
O 35-40 years
√ What is your age in years? ____ years
X Was your holiday: __ more than a week __ a week or more
√ How many days was your holiday? ______ days
Do (2) otherwise consider a response scale
X Would you recommend Rotterdam to your friends as a city
to visit? __ yes __ no __ maybe
√ How likely are you to recommend Rotterdam to your friends
as a city to visit?
Not at all likely 1 2 3 4 5 Extremely likely
√ I would definitely recommend Rotterdam to to my friends
Strongly disagree 1 2 3 4 5 Strongly agree
Do (2) otherwise consider a response scale

163. For example, semantic differential formats are used
to measure destination image
This hotel is:
exciting boring
beautiful ugly
X
X
Do (3) make it neutral, singular, and
clear
Neutral: all possible responses are equally OK
Singular: about one thing only
Clear: obvious what you mean from the
respondent’s point of view
Do not (1) use vague items
X Do you go to theme parks a lot?
How many times per year is a “lot”?

164. Do not (2) ask two things at once
X Have you ever had a good holiday experience at a
theme park and a restaurant?
Even if you get an interesting answer……which are
they referring to?
Do not (3) use categories when you don’t have to
X How many hours did
you stay at the museum?
O 0-1 hours
O 2-4 hours
O 5 or more hours
Do not (4) use categories that don’t cover all
possible respondents
X What are your religious
beliefs?
O Christian

165. O Muslim
O Atheist
Do not (5) use leading items, which suggest that
one response is better
X Do you think that the food in the hotel made you sick?
X Did the hotel staff seem unfriendly to you?
Do not (6) use “check all that apply”
X What were your reasons for visiting Amsterdam? Tick all
the boxes that apply to you
X You may select more than one reason
Design for order; helps participants answer openly
Easy Difficult
General Specific
Relevant to main topic of
study
Demographic/
personal
Closed-ended Open-ended

166. Intro
General
instructions
Content questions Demographic Thanks
beginning end
Design for clarity to make it short, simple, and neat
_______
___________
___________
___________
___________
Attractive, clear and short in length
Items grouped by topic
Color or shading for attractiveness
Give clear instructions about how to
answer the questions
Use filter questions, if necessary
Check questionnaire for good writing and grammar

167. Avoid jargon (incomprehensible to the average person)
Should travel organizations use dynamic packaging?
Catch any typographical / spelling errors
Questionaire x Questionnaire √
Comittee x Committee √
Acommodation x Accommodation √
To ensure a good response rate, introduce yourself
and explain the study
Include a brief cover letter / e-mail
– Who you are
– Who you work for
– Why you are investigating/researching
– Where you obtained the respondent’s name
– How and where you can be contacted
– Absolute guarantee of confidentiality
– Self-addressed postpaid envelope!
Dear participant,
___________
___________
________________
______
Signed, John Researcher

168. Thank you!
Testing and revising your questionnaire is
essential
Similar group of people to
your intended subjects
_________
__________
_____
Pilot test Data collection Questionnaire
Focus on respondents and
interviewers overall impressions
Revise
wording
Final polishing after
several versions to
boost response rate
A couple references for personality questionnaire
items (find more yourself)
Rammstedt, B., & John, O. P. (2007). Measuring personality in
one minute or less: A 10-item short version of
the Big Five Inventory in English and German. Journal of
research in Personality, 41(1), 203-212.

169. Gosling, S. D., Rentfrow, P. J., & Swann Jr, W. B. (2003). A
very brief measure of the Big-Five personality
domains. Journal of Research in personality, 37(6), 504-528.
And the software NHTV provides for the
questionnaire…
https://nhtv.eu.qualtrics.com
https://nhtv.eu.qualtrics.comQUESTIONNAIRE
DESIGNQUESTIONNAIRE DESIGNMost important principles
are……A questionnaire is an ordered form designed to measure
variables Questionnaires are based on the concepts from your lit
review and research questionsA questionnaire gets a number for
each variable, one at a timeWhen possible, copy your items
exactly from existing research!Do (1) get a number whenever
you canDo (2) otherwise consider a response scaleDo (2)
otherwise consider a response scaleFor example, semantic
differential formats are used to measure destination imageDo
(3) make it neutral, singular, and clearDo not (1) use vague
itemsDo not (2) ask two things at onceDo not (3) use categories
when you don’t have toDo not (4) use categories that don’t
cover all possible respondentsDo not (5) use leading items,
which suggest that one response is betterDo not (6) use “check
all that apply”Design for order; helps participants answer
openlyDesign for clarity to make it short, simple, and
neatCheck questionnaire for good writing and grammarTo
ensure a good response rate, introduce yourself and explain the
studyTesting and revising your questionnaire is essentialA
couple references for personality questionnaire items (find more
yourself)And the software NHTV provides for the
questionnaire…

170. 1
Who are you, really?
Personality
ITTI RESQN
Semester 1, Block B, 2016-2017
Ondrej Mitas & Jeroen Nawijn & Tomas Mainil
[email protected][email protected][email protected]
mailto:[email protected]
mailto:[email protected]
mailto:[email protected]
2
What makes us the same?
3
What makes us different?
4
Personality consists of individual genetic
differences in mind & behavior

171. 5
Why would we learn about personality?
6
Personality “research” dates back 1000’s of
years
Bodily fluids e.g.
blood were thought
to give people
different
personalities
7
Before the 1970’s research in personality
focused on “types,” such as “A” and “B”
Type “A” - I’m going to
go get what I want!
Type “B” - Let’s sit back
and see what happens
8

172. Two other personality types proposed were
Extroverts and Introverts
Extrovert - Let’s
hang out together!
Introvert - I need
some time to myself
9
Unfortunately, millions of consulting fees are
still earned on putting people into “types”
Verbeek, 2013
10
Looking at reality, it became obvious we
can’t put people into categories like “types”
Depending on the situation, people are
sometimes more like introverts or extroverts
See extroversion as a trait––everyone is
extroverted sometimes, but some people
more than others
11

173. Today, personality is measured as traits of
which every person has different levels
Eysenck (1947) defined the first two
personality traits as extraversion and
(emotional) stability
12
Research on personality most consistently
shows five traits (“Big Five”; McCrae & John 1992)
Extraversion
Stability (Neuroticism)
Openness
Conscientiousness (Carelessness)
Agreeableness
13
While the Big Five cover personality
generally, other traits are more specific
Sensation-seeking
(Zuckerman, 2009)
Jrubenc, 2008

174. Playfulness
(Barnett, 2007)
14 14
It is also possible to measure trait
tendencies to feel certain emotions
Some people are
happier than others
Some people get
angry easily
15
Measure emotions using scales in
questionnaires
Big Five example (Gosling, Rentfrow, & Swann, 2003)
I am: Careless, conventional……
Playfulness example (Barnett, 2007)
To what extent do the following describe you: Joking,
playful……
Emotion example (Cohn et al., 2008)
How strongly do you usually feel: Interested, joyous……

175. 16
YOUR CHALLENGE
Determine if a certain personality trait is related to a
certain kind of online behavior…
…so that an online marketing strategy can
better target a specific sort of customer
Course overview
Week Lecture Workshop
1 Introduction to course and
assignment
-
2 Survey design -
3 Population and sampling SPSS + survey
4 Data analysis -
5 - SPSS + survey
6 - -
7 - SPSS + feedback

176. 1st DEADLINE 20 January 2017
RESIT
ALT. RESIT
07 April 2017
25 August 2017
17
Please read for next lecture
• Bryman 4th edition:
Survey design
• Pp. 232-239
• Pp. 246-259
• P. 264
18
Who are you, really?��Personality�ITTI RESQN� Semester
1, Block B, 2016-2017Slide Number 2Slide Number 3Slide
Number 4Slide Number 5Slide Number 6Slide Number 7Slide
Number 8Slide Number 9Slide Number 10Slide Number 11Slide
Number 12Slide Number 13Slide Number 14Slide Number
15Slide Number 16Course overviewPlease read for next lecture
Agreeableness and perception of online hotel reviews

177. Anne-pim van Oostveen
Ondrej Mitas
Introduction
Online user-generated reviews about travel destinations, hotels,
and tourism
services have become important sources of information for
travelers (Pan,
MacLaurin, & Crotts, 2007), as each year hundreds of millions
of potential
customers consult online reviews (Vermeulen & Seegers, 2009).
Prominent
examples of consumer review platforms are websites such as
Tripadvisor and
Booking.com. Millions of global travelers share their opinions
regarding the quality
of hotels on these websites (Jeacle & Carter, 2011).
According to Chevlier and Mayzlin (2006), online user-
generated content
substantially influences the sale of products. Their study

178. suggests that the
influence of online consumer reviews is especially strong for
experience goods such
as tourism products and services, because their quality
intangible and unknown
prior to consumption. Thus, potential consumers use online
consumer reviews as a
way to reduce risk and uncertainty during a purchase (Chen &
Lee, 2008).
The consumer reviews found on travel and hospitality online
communities
provide the customer with a view of another person’s a prior
service experience.
These reviews reassure consumers that a business will deliver
the service they want
(Sparks & Browning, 2011). However, consumer trust can also
be negatively
affected, because websites also provide critical reviews.
Three-quarters of tourists consider online customer reviews as
an important
information source when planning a trip (Gretzel & Xiang,
2010). Customers see
online customer reviews as relatively unbiased and more
trustworthy than

179. marketing messages (Li & Bernoff, 2008). Ye, Law and Gu
(2009) found that
positive online reviews leads to an increase in hotel bookings.
However, research
suggests that negative information tends to have a stronger
effect on customers ().
Furthermore, not all reviews have an effect, and not all
potential customers are
affected equally.
Review sites such as TripAdvisor depend on their
trustworthiness in order to
be successful. Mayer et al. (1995) argue that trust between two
parties is based on
both the customer’s tendency to trust and the trustworthiness of
the company to
be trusted. Trust is not just based on the general trustworthiness
of the company,
but also on the tourist’s propensity to trust.
Whether someone tends to be trustful is part of their
personality. In
psychology, personality is understood to be primarily defined
by five dimensions,

180. the so-called ‘big five’: openness to experience,
conscientiousness, extraversion,
agreeableness and neuroticism (John & Srivastava, 1999; Rose
et al., 2010).
Agreeableness is the most important dimension with regards to
trust. Agreeable
individuals tend to be ‘friendly, courteous, considerate,
accommodating, tend to
avoid conflict, co-operative, helpful, forgiving and show
propensity to trust’ (Tan &
Yang, 2013, p. 27).
Research has been done on how TripAdvisor and other websites
that provide
online user-generated reviews try to engender trust (Jeagle &
Carter, 2011).
However, little research has been conducted on the influence of
customers’
propensity to trust and the perception of online reviews. In this
article, I will
investigate the relation between the personality trait of
agreeableness and the
perception of online hotel reviews.

181. Therefore, the main research question is: what is the
relationship between
agreeableness and the perception of online hotel reviews? More
specifically, I
examine the following sub-questions:
1. Is agreeableness positively related to use of online reviews
before booking a
hotel?
2. Is agreeableness positively related to trust of online hotel
reviews?
3. Is agreeableness positively related to booking flexibility in
the presence of
positive reviews?
4. Is agreeableness positively related to booking willingness in
the presence of
negative reviews?
Methods
To investigate the relation between agreeableness (independent
variable)
and perception of online reviews (dependent variable), an online
survey was
carried out.

182. Population and sample
The population for this study is defined as Dutch students who
have Internet
access, use Facebook and are between the age of 18 and 25.
This population was
selected for their availability and the fact that they are likely to
use internet
applications such as review platforms.
Convenience sampling was used and data was collected using an
online
survey facility (http://www.survio.com). The self-administered
questionnaire was
published as a status update on Facebook. In total, 46 people
answered the
questionnaire. Nine participants did not fit the population
criteria and were
excluded from further analysis. Thus, the final sample size was
37. Of these
participants, 26 (70%) were women and 9 (30%) male. The fact
that 70% of the
sample is female is a substantial diversion from the population
of Dutch students,

183. which is nearly evenly divided by gender (48% vs. 52%; CBS,
2012, p. 101). As no
sampling frame was used, response rate could not be
determined. However, 51% of
page visitors started and completed the questionnaire. All
participants answered
all questions. For the purposes of the assignment, the total
number of participants
was artificially inflated from 37 to 300 participants.
Questionnaire
The questionnaire consisted of three parts. The first part of the
questionnaire contained questions to find out participants’ level
of agreeableness.
To measure this a 10-item short version of the Big Five
Inventory was used
(Rammstedt & John, 2006, p. 210). The participant was asked to
rate the
statements on personality on a five-point scale from “disagree
strongly” to “agree
strongly”. Items 2 and 7 from the scale were used to measure
agreeableness. The
polarity of question 7 was reversed.

184. The second part of the questionnaire consisted of questions on
online review
perception and use. Items on this topic were specifically created
for this research
since no available questionnaire items were found during
literature review. A
seven-point scale from ‘never’ to ‘always’ was used to measure
the frequency of
use of online reviews before booking a hotel. Subsequent items
asked participants
to rate statements regarding online hotel reviews on a five-point
scale from
‘disagree strongly’ to ‘agree strongly’.
To measure trust in online reviews, participants were asked to
rate the
following on an agreement scale:
Online hotel reviews on websites such as TripAdvisor are…
1. Trustworthy?
2. Reliable?
To determine whether positive reviews would make choices of
customers more

185. flexible, respondents were asked to rate the following trade-
offs:
If the reviews of an hotel are very positive...
1. I’m willing to pay more money
2. I don’t mind a less favorable location
Finally, willingness to book in spite of negative reviews was
measured with the
item: ‘When I read a negative review on the hotel I wanted to
book I don’t let it
influence my decision.’ In addition to the research variables, the
demographic
variables of gender, age and education were measured using a
multiple-response
format.
Analyses
As all variables in this study were measured at the interval
level and were
thus described in terms of means and standard deviations. I used
Pearson
correlation tests to assess relationships between variables.
Findings
Descriptive findings are first reported for the main variables of

186. agreeableness, use of online reviews and trust. Subsequently,
the relationship
between agreeableness and the dependent variables is
investigated using
correlational analysis.
Agreeableness
Responses to two five-point scale questions from the big five
inventory were
averaged (controlling for polarity) to measure agreeableness.
The lowest possible
score for agreeableness was 1 and the highest 5. On average
participants scored
3.70 on agreeableness. This is a slightly higher than a neutral
score of 3. Standard
deviation for this variable was 0.75, indicating that the
responses were relatively
close together.
Use of online reviews before booking a hotel
To measure use of reviews a seven-point interval scale was
used. On
average, the participants scored 4.73. Thus, given that the

187. midpoint of the scale is
4, many participants frequently use online reviews. The
standard deviation was
1.67, indicating that the responses were neither far apart nor
close together.
Trust in online reviews
To potentially increase reliability, responses to the two five-
point scale
questions on ‘trustworthiness’ and ‘reliability’ of online
reviews were averaged to
measure trust in online reviews. This new variable comprised an
interval scale from
1 to 5. The mean response was 3.33, well above the midpoint of
the scale.
Standard deviation for this variable is 0.77, indicating that the
responses were
relatively close together.
Booking flexibility with positive reviews
Participants were asked about flexiblity related to price and
location in the
presence of good reviews on a five-point scale. On average, the
participants
reported mean flexibility of 3.05 concerning price and 2.84

188. concerning location.
Thus, participants are somewhat flexible in booking a
positively-reviewed hotel.
The standard deviations regarding price and location were 0.96
and 0.97
respectively, indicating that the responses were relatively close
together.
Willingness to book in spite of negative reviews
Participants rated their willingness to book a negatively-
reviewed hotel on a
five-point scale. Participants reported an average willingess of
2.69, suggesting
ambivalence about booking in the face of negative reviews. The
standard deviation
was 0.9, indicating that the responses were relatively close
together.
Effects of agreeableness on review use and trust
Correlations are interpreted according to the following rules of
thumb
(Bryman, 2012): any value above r = 0.5 is considered a large
correlation, 0.3-0.5 is
moderate and 0.1-0.3 is small (<0.1 is negligible).

189. There was a small significant relation between agreeableness
and use of
online hotel reviews (r = .12, p = .04). Participants who were
more agreeable,
indicated that they made more use of online reviews. The
correlation between
agreeableness and trust in online reviews was not significant (r
= .06, p = .27).
To understand how positive reviews influence flexibility in
booking a hotel
room, the participants were confronted with trade-offs about
money and location.
The results show that participants who scored high on
agreeableness were
significantly less likely to accept a higher price or a less
favorable location when
presented with very positive reviews (price: r = -.19, p = .001;
location: r = -.18, p
= .001). The correlation coefficients are negative with an
absolute value between
0.1 and 0.3, indicating a small effect. This counterintuitive
result will be

190. elaborated on in the discussion.
The questionnaire also included an item on how negative
reviews influence
willingness to book a hotel. The results show that people who
score high on
agreeableness are significantly less likely to disregard negative
reviews in their
decision (r = -.37, p < 0.001). In other words, this moderate
correlation showing
that highly agreeable people are more influenced by negative
reviews.
Discussion
The present findings reveal the relation between agreeableness
and the
perception of online hotel reviews. Previous research describes
how review
websites try to create a trustworthy appearance (Jeagle &
Carter, 2011). This
study adds to existing literature by investigating the trust
relationship between
hotel review website and the customer from the customer
perspective.

191. No significant correlation between agreeableness and trust in
online hotel
reviews was found. This could be because a consumer’s
personality structure does
not have much of an influence on their trust in online hotel
reviews. However, the
results of the research show that there is a small correlation
between
agreeableness and the use of online hotel reviews. This effect
could be due to a
different aspect of the agreeableness trait than trust, such as
valuing the opinions
of others (Tan & Yang, 2013).
Furthermore, findings show that there is a significant moderate
correlation
between agreeableness and the influence of negative reviews.
While a relationship
between agreeableness and trust of online hotel reviews in
general was not found,
agreeableness may lead to a higher level of trust in negative
reviews specifically.
Therefore, agreeable individuals might be quicker to believe
negative reviews and
thus these reviews could have a bigger influence on the decision

192. not to book a
hotel.
The finding showed that people who score high on
agreeableness were
significantly less likely to accept a higher price or a less
favorable location when
presented with very positive reviews on a hotel was
counterintuitive. This might
be explained by the idea that agreeable people are more
considerate (Tan & Yang,
2013, p. 27). Because they are more considerate they might
consider other choice
criteria such as price and location as more important than a very
positive review.
Limitations
A possible limitation of this study is that only two questionnaire
items were
used to measure agreeableness. The items were taken from an
already abbreviated
Big Five personality test. Therefore, the reliability of this study
could have been
enhanced by asking participants to rate more items regarding

193. agreeableness.
Furthermore, questions on online hotel review perception were
not taken from
other scientific articles, because they were specific for this
study.
Convenience sampling was used for this study, so the sample
does not
represent the population. Only people who are my friends on
Facebook were able
to fill in the questionnaire. My Facebook friends that are
studying are not a
representative sample of the actual Dutch student population.
Furthermore, the
nature of voluntarily filling in a questionnaire attracts and
excludes certain types
of people from the sample.
A final point that is important to mention is the fact that
declaring intention
is not the same as actually acting on it. People might choose
answers in the
questionnaire that are different from their reaction when they
are presented with
the same situation in real life. For example, people who say that
they don’t let

194. negative reviews influence their decision on booking at a hotel,
might actually be
highly influenced by a negative review when they have to make
a real hotel
reservation that involves spending their own money in that
moment.
Implications for the travel industry
There is a large body of research on tailoring website content to
user
profiles (Calegari & Pasi, 2013; Pera & Ng, 2013; Bobadilla et
al., 2012). User
profiles are constructed from digital data that is gathered by the
website and
external third parties. By creating user profiles and sending
tailored information
towards potential customers, online marketers can better target
their marketing
messages. The user profile is a collection of data that represents
the user’s
identity.
According to Klimstra et al, 2013, p. 213: ‘Personality is among
the most

195. important factors contributing to individual differences in
identity formation’.
Therefore, collecting data on a user’s personality is vital in
creating a user profile.
This study can be seen as a first step in investigating the
relation between
consumer personality and the perception of online hotel
reviews. Commercial
enterprises such as TripAvisor and Booking.com could use this
study as a blueprint
to investigate how consumers with a certain personality types
react to the content
on their website. By knowing how a consumer will react to the
content, the
content can be adjusted in order to create the desired reaction.
Combined with user profiling to find out the consumer’s
personality, this
would create the opportunity to identify a consumer’s
personality type and adjust
the content on the website based on his/her personality. On
websites such as
TripAdvisor consumers would see tailored information that fits
their personality

196. and will result in a reaction that has been researched before,
much like approaches
increasingly taken by Facebook and OKCupid.
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Appendix
Questionnaire
SPSS Tables

201. 1
Determinants of daily happiness on vacation
Jeroen Nawijn
2
Introduction
The need for a happy holiday
Tourists take holidays for pleasure and when doing so, they
implicitly
assume that vacationing makes them happier. Recent research
reveals that
individuals indeed benefit from vacationing in terms of
happiness. Anticipation of a
holiday trip leads to higher feelings of happiness (Gilbert and

202. Abdullah 2002;
Hagger and Murray 2009; Nawijn et al. 2010), while post-trip,
tourists experience
less stress and are in generally better health, although these
positive effects are
short-lived (De Bloom et al. 2009). When returned home,
happiness is only
significantly higher for those who had a very relaxed holiday
experience (Nawijn et
al. 2010).
Tourism managers have a range of tools available, which
inform them about
customer behavior and attitude. For instance, image research
informs managers
how customers view a company or a holiday destination.
Furthermore, customer
loyalty programs, such as clubs, frequent flyer programs or
online communities are
also frequently used in the tourism industry. An even more
often used tool for
tourism management information is customer satisfaction
research. This frequently
and elaborately studied area (González, Comesaña, and Brea
2007; Alegre and

203. Garau 2010; Heo, Jogaratnam, and Buchanan 2004; Kim, Ma,
and Kim 2006; Nash,
Thyne, and Davies 2006; Neal and Gursoy 2008; Oh and Parks
1997; Weiermair and
Fuchs 1999; Wu 2007) concentrates mostly on the post-hoc
appraisal of tourism
products and services. It often does not provide any information
on how tourists
actually felt during their holiday trip. If feelings are taken into
account, these are
generally considered to the extent how they affect post-hoc
appraisal (De Rojas
and Camarero 2008; Gountas and Gountas 2004; Sirakaya,
Petrick, and Choi 2004)
3
or how emotional elements of customer satisfaction are related
to overall
satisfaction (De Rojas and Camarero 2008; Del Bosque and San
Martin 2009).
Data on the psychological aspects of the tourist experience

204. (Larsen 2007)
appear to be almost non-existent. The existing studies on
emotions during a trip
focus on post-hoc assessment of emotional experiences (Hosany
and Gilbert 2009)
or on emotions as predictors of customer satisfaction (Bigné,
Andreu, and Gnoth
2005; Zins 2002). Even studies on the relation between holiday
trips and subjective
well-being focus mostly on post-trip appraisals (Filep 2008;
Milman 1998; Neal
2000), or on comparisons of pre- and post-trip appraisals
(Besser and Priel 2006;
Gilbert and Abdullah 2004).
The post-trip/recollection phase of a holiday trip (Clawson and
Knetsch
1966) involves savoring the holiday experience (Bryant and
Veroff 2007), but such
recollections are deemed unreliable (Braun-LaTour, Grinley,
and Loftus 2006;
Kemp, Burt, and Furneaux 2008). Although these unreliable
recollections may add
to wider happiness of vacationers in their everyday lives, this
rosy view (Mitchell et

205. al. 1997) distorts holiday memories and thus tourism managers
do not have
genuine insight into the actual experience of a holiday trip as it
is taking place.
Such insight is crucial, however, to the quality of the tourism
product, to
competitive advantage, and eventually to higher profits. Pine
and Gilmore
emphasized that managers “must focus on the experience
customers have while
using their goods” (Pine II and Gilmore 1999, p. 15). Tourism
management is
currently lacking such an insight. Moreover, tourists do not
have such information
either. If holidays mostly do not bring about a post-trip
happiness boost (Nawijn et
al. 2010), perhaps consumers are better off spending their
money on other things
than holidays.
4

206. Unfortunately, virtually nothing is known about individuals’
levels of positive
and negative emotions during a holiday trip. We do know that
tourists tend to feel
generally good during their holiday trip (Nawijn, 2010), but
slightly worse at the
start of their holiday (Pearce 1981; Nawijn, 2010), which is
when tourists report
more health problems compared to other days of their trip
(Cartwright 1992; Kop
et al. 2003).
Research questions
This paper adds to the existing literature by addressing the
following
questions: (1) How do tourists feel during a day of their holiday
trip? (2) What
affects these feelings? (3) Do they feel better on holiday
compared to their
everyday lives?
Methods
Respondents
Data were collected on different days of the week at 12

207. different tourism
locations in the Netherlands during 13 days in the months of
April, May and June of
2009. These locations included popular international tourism
venues in Amsterdam
and locations such as the Keukenhof, Delft, Scheveningen,
Maastricht and
Valkenburg. The choice of these locations was based on a list of
the 20 most
visited attractions in the Netherlands (Netherlands Board of
Tourism & Conventions
2006). A convenience sample of 466 international leisure
travelers filled in a self-
report questionnaire. Respondents originated from 51 countries.
The sample was
relatively young; 60% were between 18 and 29 years of age.
Men made up 50% of
the sample.
Measures
5

208. Happiness. The dependent variable of this study is emotion,
which
Veenhoven (1984) sees as the ‘affective dimension’ of
happiness. Emotions can be
measured using a list of positive and negative such the PANAS
(Watson, Clark, and
Tellegen 1988). Emotion balance is the difference between the
amount of positive
and negative feelings experienced. In this study we assessed 12
emotions taken
Kahneman et al. (2004); four positive emotions: happy,
competent/capable,
warm/friendly and enjoying myself, and eight negative ones:
impatient,
frustrated/annoyed, depressed/blue, hassled/pushed around,
angry/hostile,
worried/anxious, criticized/put down and tired. Respondents
stated how they felt
on the day they filled in the questionnaire, using a 7 step rating
scale for each
emotion ranging from “not at all” to “very much”. For each
respondent I computed
average positive emotion and average negative emotion and
subsequently

209. subtracted the latter from the former. The possible range of the
resulting scale is
−6 (average positive 1, negative 7) to +6 (average positive 7,
negative 1).
Holiday stress. Respondents were asked to state how stressed
they felt on a
7-point scale.
Activity. An open-ended question was included; respondents
could state
their most important activity of the day. These were later
grouped into tour,
museum visit, travel, sightseeing, going out, relaxing or other
type of activity.
Travel party. The number of people in the travel party, attitude
towards
the travel party on a 5-point scale and type of travel party
(alone, partner,
relatives, friends, colleagues, other) were all assessed.
Type of holiday. Respondents had to state what type of holiday
trip they
had booked. The response alternatives comprised a cultural
holiday, a nature

210. 6
holiday, a city trip, a beach holiday, a cruise, an event holiday,
a tour, or some
other type of holiday.
Temperature. The mean outside temperature of the day was
included as a
variable, as it is known that temperature affects how people feel
(Keller et al.
2005). Mean temperature scores were obtained from the website
of the Royal
Dutch Meteorological Institute.
Part of the trip. As day of the week and length of trip are both
associated
with daily happiness on vacation (Nawijn, 2010), both were
assessed.
Time of day. The hour of the day in which respondents filled in
the
questionnaire was registered, because, in everyday life, time of
day tends to be
associated with emotion (Csikszentmihalyi and Hunter 2003;
Egloff et al. 1995;

211. Stone et al. 2006).
Socio-demographic variables. Age, income, and gender were all
assessed.
Analyses
As all data besides gender, activities, travel party type and
holiday type
were measured at the interval level, variables of interest were
described in terms
of means and standard deviations. Relationships between
interval-level variables
were tested using Pearson correlation tests. Effects of nominal-
level variables such
as activities and holiday type on emotion balance were tested
using ANOVAs.
Findings
Daily feelings on vacation
Average positive emotion (M = 4.74, SD = .81, n = 445) was
much higher than
average negative emotion (M = 1.09, SD = .84, n = 426),
resulting in a mean
emotion balance of 3.67 (SD = 1.36, n = 419). The low mean
and standard deviation

212. of negative emotion shows that hardly any participants felt
negative emotion.
7
Positive emotion was moderately high and, like negative
emotion, scores were not
widely dispersed. Emotion balance had a somewhat higher
standard deviation,
indicating scores that were somewhat more spread out.
Emotion balance was significantly associated with age (r =
+.25, p < .01, n =
418), income (r = +.27, p < .01, n = 413), holiday stress (r =
−.55, p < .01, n = 419)
and attitude towards the travel party (r = +.31, p < .01, n =
395). These
associations were all mild or moderate and positive, except the
correlation with
holiday stress, which was strong and negative. The variables of
temperature, day
of the trip, length of stay, time of day and number of people in
travel party were

213. not significantly associated with emotion balance, although a
higher number of
persons in a travel party was mildly positively associated with
positive emotion (r =
+.12, p < .05, n = 440).
Group differences
One-way analyses of variance indicated no significant
differences in emotion
balance between different activities of the day (tour, museum
visit, travel,
sightseeing, going out, relaxing, other type of activity) or type
of holiday (cultural
holiday, nature holiday, city trip, beach holiday, cruise, event
holiday, tour, other
type of holiday). Levels of emotion balance did not differ in
men or women either.
A one-way between groups analysis of variance did identify
significant
differences in emotion balance between types of travel party, F
(5, 411) = 5.4, p <
.01. Post-hoc comparisons using the Tukey HSD test indicated
that the mean
emotion balance for colleagues as travel party was significantly
lower than other

214. types of travel party. In other words, respondents traveling with
colleagues felt
significantly less positive than other respondents.
Comparison to everyday life
8
The mean emotion balance of the current study was compared to
individuals’ mean emotion balance in everyday life. The latter
score was derived
from the World Database of Happiness. The mean emotion
balance in everyday life
was calculated by using data on all 40 countries listed in that
particular section of
the database (Veenhoven 2010). Since these scores are on a 0-
10 range, linear
scale transformation had to be used on the current study’s
emotion balance to
allow for accurate comparison of mean scores. The mean
emotion balance while
on holiday (M = 8.06) was significantly higher (p < .01) than

215. the mean score
observed in everyday life (M = 6.30).
Discussion
The tourist experience
The present findings show that tourists are generally happy
during a day of
their trip. This corroborates recent research (Nawijn, 2010; De
Bloom et al. 2010),
which found that tourists are generally happy during their
holiday. Emotion
balance while on vacation is significantly higher than in
everyday life. Thus,
tourists feel generally better on holiday compared to everyday
life.
Tourists’ daily emotion balance during the holiday is only
partially
determined by their socio-demographic backgrounds. Most of
the variance in
emotion balance was explained by variables associated with the
holiday trip itself.
Holiday stress and attitude towards the travel party were the
strongest predictors
of tourists’ feelings during a day of their holiday trip. The

216. importance of holiday
stress was already acknowledged in a recent study by Nawijn et
al. (2010). Their
study found that holiday stress affects vacationers post-trip
happiness. The current
study shows that holiday stress is also detrimental to happiness
during the holiday
itself.
9
Implications
Tourism managers are generally doing a good job, as most
tourists feel good
during their holiday trip. Management could further enhance the
psychological
experience of a holiday trip by trying to reduce holiday stress.
Long travel periods
are apparent stressors. However, reducing the length of travel
will most likely be
difficult with most trips. Jet lag (Stokes & Kite, 1994) and
health problems related

217. to air travel may also cause holiday stress (Vingerhoets,
Sanders, and Kuper 1997).
Airlines or tour operators could provide better information on
how to prevent jet
lag or other health issues. Where possible, tourists should try to
avoid such
situations. Furthermore, they need to think about whom they
want to go on
holiday with. Finding the right travel party could greatly
enhance their tourist
experience.
The current study also shows that more research is needed on
emotions
during a holiday trip. The tourism industry should include more
emotional
components in their questionnaires, as post-hoc customer
satisfaction studies do
not provide a genuine insight into the consumer experience
while it is taking place.
Limitations
The current study focused mostly on between-person
differences, as
opposed to within-person differences. For practical reasons it

218. was not possible to
question respondents several times throughout the day or in
their everyday lives.
Adopting such an approach may produce somewhat different
results. Furthermore,
the fact that days for data collection as well as respondents
themselves were
selected based on convenience means that the sample was not
random. Therefore,
it is not possible to generalize from the respondents to a larger
population. To do
so, a probability sample would be necessary.
10
Future research
More research is needed on the causes of holiday stress.
Depending on the
individual, certain events are deemed more stressful than others
(Cohen, Kamarck,
and Mermelstein 1983; Lazarus 1999). Future research should
assess the extent to

219. which certain aspects of holiday trips or certain types of trips
are deemed more
stressful than others and on finding the right person-
environment fit. Specific
attention could be paid to things tourists worry about (Larsen,
Brun, and Ogaard
2009).
Additional work needs to be done to further explore the relation
between
real-time emotion balance and post-hoc feelings, and the
willingness to purchase
tourism products in the future. Research among vacationers who
were on holiday
showed that consumption emotions are linked to loyalty and
willingness to pay
more (Bigné and Andreu 2004; Bigné, Andreu, and Gnoth
2005). Another study, on
spring break vacations, found that remembered experience,
compared to
predicted experience and actual experience, is a stronger
predictor of the desire
to repeat a vacation (Wirtz et al. 2003). The importance of
holiday stress during a

220. trip, as found in this study, and its influence on post-trip
emotion scores, as found
in another study (Nawijn et al. 2010), suggests a possible
moderate to strong
connection between on-trip feelings and post-trip feelings.
Further research is
required to establish this potential link.
Finally, to further assess how important a holiday trip may be
for
individuals, it is necessary to compare tourists’ daily feelings
during everyday life
to their daily feelings during a vacation. A recent study by
McCabe (2009) found
that domestic duties were perceived as more pleasurable while
on holiday,
compared to when performed at home. Therefore, it would be
particularly
11
interesting to examine whether ordinary experiences, such as
talking with

221. relatives, or common activities (e.g., grocery shopping or
cleaning), which take
place on holiday as well as in everyday life, are experienced
differently–in terms of
happiness–when in a different setting. This would shed more
light on how the
psychological experience of holiday is really different from
everyday life.
12
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